Impact of Stress Ulcer Prophylaxis Algorithm Study
Bibliographic record
Abstract
BACKGROUND: In the intensive care unit at Royal Victoria Hospital, we noted that drugs prescribed for stress ulcer prophylaxis were not always indicated or optimal. Accordingly, we implemented an algorithm for stress ulcer prophylaxis to guide the medical team in their decisions. The agents selected for the algorithm were intravenous famotidine and omeprazole suspension or tablets, depending on the available administration route. OBJECTIVE: To evaluate the impact of a treatment algorithm on the appropriateness of prescriptions for stress ulcer prophylaxis. METHODS: A quasi-experimental-type evaluative study was conducted based on a pre-/post-intervention design without a concurrent control group. A total of 555 complete admissions met the selection criteria; 303 patients formed the pre-intervention group, and 252 made up the post-intervention group (exposed to the treatment algorithm). RESULTS: After implementation of the algorithm, the proportion of inappropriate prophylaxis was decreased (95.7% vs 88.2%; p = 0.033). The number of days of inappropriate prophylaxis was also reduced significantly (p = 0.013), as was the cost per patient (p = 0.003) for all admissions. However, no difference was observed when the subgroup of patients who received prophylaxis alone was studied (p = 0.098 and p = 0.918). The presence of bleeding was similar in both groups. CONCLUSIONS: Introduction by pharmacists of a treatment algorithm for stress ulcer prophylaxis in intensive care units allows a reduction of inappropriate prescriptions and thus a reduction in the cost of drugs. The use of omeprazole suspension seems to be an alternative to intravenous histamine2-inhibitors; however, a large-scale study is necessary to confirm the efficacy and safety of proton-pump inhibitors administered by an enteral tube.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".